How AI-assisted Text Analytics Reveals Actionable Insights

DZone 's Guide to

How AI-assisted Text Analytics Reveals Actionable Insights

AI-powered text analytics software is a part of the customer data platform as it helps businesses to gauge customer sentiment for making better decisions.

· AI Zone ·
Free Resource

Raw unstructured textual data extracted from emails, blogs, tweets, feedbacks, etc. holds immense business value. After performing a high-level analysis, this data can unlock several business opportunities ranging from new product ideas to stronger customer relations. Artificial intelligence (AI) technologies like machine learning, deep learning, and cognitive search further improve data analysis and reveal previously unknown correlations.  

These technologies are proving to be effective at performing an extensive analysis of textual communications. AI-powered text analytics enables businesses to gauge customer sentiment for making better decisions that improve customer relations and drive revenue.

In this article, we will explore the nitty-gritty of text analytics and its benefits for eCommerce businesses.

Significance of Unstructured Data



img 1

Source: TechTarget 

Text in any form, be it tweets, feedbacks, reviews, blogs, emails, social media conversations, etc. can reveal valuable information about the customers’ likes/preferences and buying habits. The availability of an effective data analytics solution enables businesses to significantly improve their day-to-day operations. Customer feedback data is growing exponentially creating challenges for businesses to back their decisions with insights. Big data statistics show that two-thirds of the data generated today is by individuals rather than companies and enterprises. A majority of this data is generated through social media platforms in the form of views, comments, posts, etc. Such user-generated data has high business potential with unique opportunities waiting to be discovered. 

Lack of resources makes it difficult for businesses to manage and analyze this data that can help them make key business decisions. Moreover, this data is generated daily so it is critical to be able to understand and extract valuable information from it.

For eCommerce businesses, understanding customer needs and issues can lead to delivering smooth customer experiences. Textual communications with customers can provide actionable insights to improve services and deliver better shopping experiences to customers. 

Let us understand how text analytics can bring a whole new perspective to way eCommerce businesses improve customer relations and drive sales.

Customer Analytics

Customers communicate with brands in a number of ways including calls, messages, reviews, feedback, etc. These communications occur on various platforms such as social media, forums, company’s chatbots, and customer support helplines. The transcripts of calls, the mentions of the brand on social media, forums, the interaction with chatbots, and reviews of the products can provide directions for business improvements. By using AI-assisted text analytics, businesses can spot patterns in data from these sources and act on it to improve customer relations.



img 2

Source: ResearchGate

The image shows how AI technology works behind the scenes and provides valuable insights.

Sentiment Analysis

Analyzing emails, chatbot conversations, feedback on products, and reviews can provide a closer look at the customers’ sentiments. Manual analysis is not only time-consuming but prone to errors too. So, using an AI-powered tool is essential to decoding the sentiments behind textual communications on a regular basis. eCommerce businesses can sense what’s wrong with a particular product that is receiving negative reviews consistently and can take necessary action to resolve the issue. Sentiment analysis is essential for businesses to analyze customer behavior and improve their customer relations.

Deep learning and machine learning mechanism lend cognitive abilities for a sentiment analysis tool to produce accurate results. They play a critical role in identifying patterns in customer conversations and open doors to new business opportunities. 

Text Classification

training to operation


Source: ResearchGate

Another key aspect of text analytics is categorization or classification. It involves filtering and segmenting content based on broad terms and then narrowing it down for deeper analysis. The text classification feature can enable businesses to adjudge the ongoing reputation of their brand or certain products. Given the huge datasets available for analysis, classification/categorization can improve analysis and enhance decision-making processes.

Businesses can also identify the most critical areas that require immediate action to improve customer satisfaction. In this way, they can enhance customer engagement and accelerate revenue.

Benefits of AI-assisted Text Analytics

COVID-19 has cast a huge shadow of uncertainty on the entire world, impacting individuals and businesses alike. With people living indoors and spending a major chunk of their daily lives in front of their desktop and mobile screens, businesses are changing the way they work. eCommerce businesses are witnessing a huge surge in demand for essential items. It is critical for them to step up their customer relationship management efforts to be competitive. To that effect, an AI-powered tool for analyzing text can make a significant impact.

The following are the benefits of using text analytics:

  1. Provides insights to improve customer relationships and experiences
  2. Enhances product development and customer retention efforts
  3. Classifies conversations so you can make prioritized decisions for maximum impact

Conducting text analytics is akin to listening to your customer for being attentive to their concerns and building on their preferences. 

ai, artificial intelligence, customer analytics, ml, sentiment analysis, text analytics

Published at DZone with permission of Skellam AI . See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}